An Investigation into Automatic Photometric Calibration

Abstract

Photometric calibration is a critical process that ensures uniformity in brightness across images captured by a camera. It entails the identification of a function that converts the scene radiance into the pixel values in an image. The goal of the process is to estimate the three photometric parameters - camera response function, vignette, and exposure.

A significant challenge in this field is the heavy reliance on ground truth information in current photometric calibration methods, which is often unavailable in general scenarios. To address this, we investigate our proposed simple method, New Photometric Calibration (NPC), which eliminates the need for ground truth data. Firstly, we integrated our photometric calibration algorithm with long-term pixelwise trackers, MFT, enhancing the system’s robustness and reliability. Since the MFT effectively handles occlusion and reduces drifting, it results in a more stable trajectory. By incorporating MFT to track feature points across frames and using the trajectory as corresponding points, we can utilize the pixel intensity of corresponding points to forgo the need for exposure ground truth during initialization. Subsequently, we independently optimize the photometric parameters to sidestep the exponential ambiguity problem.

Our experiments demonstrate that our method achieves results comparable to those utilizing ground truth information, as evidenced by comparable root mean square errors (RMSE) of the three photometric parameters. In scenarios without ground truth data, NPC outperforms existing methods. This indicates that our approach maintains the accuracy of photometric calibration and can be applied to arbitrary videos where ground truth information is not provided.

In conclusion, our research represents a significant advancement in the field of photometric calibration. We investigate a novel and effective method that requires no ground truth information during the photometric calibration process. Our approach incorporates the use of a robust tracker, enhancing the trajectories of feature points, thereby improving the overall performance of our method. Furthermore, our model not only bypasses the exponential ambiguity problem inherent in the optimization process but also addresses the challenges associated with the traditional reliance on ground truth information, outperforming previous photometric calibration methods when the input lacks ground truth data.